A competitive-equilibrium model of PoUW with ML inference yields closed-form allocations across three regimes (Bitconia, Fortessia, Duplexia) where attack costs remain tied to block rewards and token incentives can subsidize additional inference.
Proofs of useful work from arbitrary matrix multipli- cation.CoRR, abs/2504.09971, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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Pearl's 24 EH/s cuPOW network produces zero useful AI computation because the dominant mining software contains no inference code and the verification protocol accepts random matrices by design.
Bitwise-precise re-computation of LLM inference across GPU variants is achievable via software-only emulation, allowing rounding errors to serve as auditable signatures of the inference setup.
citing papers explorer
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The Economics of Proof-of-Useful-Work
A competitive-equilibrium model of PoUW with ML inference yields closed-form allocations across three regimes (Bitconia, Fortessia, Duplexia) where attack costs remain tied to block rewards and token incentives can subsidize additional inference.
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The Usefulness Gap in Proof-of-Useful-Work: An Empirical Study of Pearl's cuPOW Protocol
Pearl's 24 EH/s cuPOW network produces zero useful AI computation because the dominant mining software contains no inference code and the verification protocol accepts random matrices by design.
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Bit-Exact AI Inference Verification Without Performance Tradeoffs
Bitwise-precise re-computation of LLM inference across GPU variants is achievable via software-only emulation, allowing rounding errors to serve as auditable signatures of the inference setup.